SPATL-XLC: An Explainability-Driven Framework for Efficient and Robust Federated Learning Under Non-IID Data by Samuel Hailemariam Seifu, Beakal Gizachew Assefa

“…Federated learning (FL) enables multiple devices to collectively train a machine learning model without sharing private data. However, when data across devices differ significantly (non-IID),training becomes less accurate and difficult to understand in terms of how the model makes decisions (explainability). …”
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Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data by Hanbin Song, Sanghyeop Yeo, Youngwan Jin, Incheol Park, Hyeongjin Ju, Yagiz Nalcakan, Shiho Kim

“…Our method leverages SWIR’s distinct reflectance characteristics, particularly for materials containing moisture, and demonstrates a significant improvement in accuracy. Specifically, SWIR data achieved near-perfect classification results with an accuracy of 99% for distinguishing real from artificial objects, compared to 77% with visible spectrum data. …”
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An iterative approach for estimating domain-specific cognitive abilities from large scale online cognitive data by Valentina Giunchiglia, Dragos-Cristian Gruia, Annalaura Lerede, William Trender, Peter Hellyer, Adam Hampshire

“…We propose that IDoCT can enhance the precision of online cognitive assessments, especially in large scale clinical and research applications.…”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

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Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production by Christian Friedrich, Stefan Vogt, Franziska Rudolph, Paul Patolla, Jossy Milagros Grützmann, Orlando Hohmeier, Martin Richter, Ken Wenzel, Dirk Reichelt, Steffen Ihlenfeldt

“…GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.…”
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Winescape perception and big data analysis: An assessment through social media photographs in the Chianti Classico region by Veronica Alampi Sottini, Elena Barbierato, Iacopo Bernetti, Irene Capecchi, Sara Fabbrizzi, Silvio Menghini

“…Keywords: Winescape, Big data, Landscape quality, Image clustering, Maxent, Wine tourism: territorial marketing…”
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Research on Discrete Dynamic System Modeling of Vocal Performance Teaching Platform Based on Big Data Environment by Xiaohui Gong

“…The results show that the optimization model based on the deep recurrent neural network algorithm has the advantages of high feasibility, high intelligence, and wide range of applications. The experiments show that the deep recurrent neural network algorithm can analyze the effectiveness of the innovation model, and the fuzzy evaluation method can realize the comprehensive evaluation of the innovation mode, which is conducive to the improvement of students’ vocal music learning ability.…”
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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method by Ankit Kumar, Kamred Udham Singh, Manish Kumar

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PDA: A Novel Privacy-Preserving Robust Data Aggregation Scheme in People-Centric Sensing System by Ziling Wei, Baokang Zhao, Jinshu Su

“…As sensor nodes need to send their individual sensed data to an aggregator node and these data are related to users' real life, privacy-preserving data aggregation is a challenge issue. …”
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Parcel-level vector data for scaled land utilization analysis in Xinjiang based on remote sensing image by Wei Wu, Yikai Zhao, Liao Yang, You Zeng, Rui Liu, Shuangyan Huang, Weisheng Wang, Xiande Wu

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High-Precision Experimental Data for Thermal Model Validation of Flat-Plate Hybrid Water PV/T Collectors by Fahad Maoulida, Rabah Djedjig, Mourad Rahim, Mohamed Aboudou Kassim, Mohammed El Ganaoui

“…The results show a good correlation between the experimental data and the model’s predictions. The average deviation observed for the outlet fluid temperature is 0.1 °C during the day and 1.3 °C at night. …”
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Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection by Wei Huang, Yongjie Li, Zhaonan Xu, Xinwei Yao, Rongchun Wan

“…Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. …”
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Improving Multi-Label Emotion Classification on Imbalanced Social Media Data With BERT and Clipped Asymmetric Loss by Sandhya Ramakrishnan, L. D. Dhinesh Babu

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Robust fault detection and severity classification in rotating machinery using VMD-LSTM for limited data scenarios by Ammar Mrabti, Ramdane Younes, Nouredine Ouelaa, Tarek Kebabsa, Zakarya Ouelaa

“…However, it faces difficulties due to complex, noisy fault signatures, non-stationary behavior, and the impracticality of obtaining large labeled datasets, limiting the effectiveness of both traditional and deep learning-based methods in real-world applications. This paper introduces a novel approach that combines Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks to improve gear and bearing defect detection, filling a gap in fault diagnostics by effectively handling limited training data. …”
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Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection by Kevin Thomas, Ahasanur Rahman, Wesam Rohouma, Md. Faysal Ahamed, Fariya Bintay Shafi, Md. Nahiduzzaman, Amith Khandakar

“…A Random Forest classifier trained on this dataset achieved an accuracy of 99.82%, demonstrating its suitability for real-time fault diagnosis and predictive maintenance applications. Unlike existing datasets, this collection offers synchronized electrical and mechanical sensor data, enabling advanced cross-sensor fault analysis. …”
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Visual analysis of multi-subject association patterns in high-dimensional time-varying student performance data by Lianen Ji, Ziyi Wang, Shirong Qiu, Guang Yang, Sufang Zhang

“…Finally, we conduct a case study using real student performance data to verify the applicability and effectiveness of MAPVis.…”
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Enhanced Broad-Learning-Based Dangerous Driving Action Recognition on Skeletal Data for Driver Monitoring Systems by Pu Li, Ziye Liu, Hangguan Shan, Chen Chen

“…This paper proposes a novel method based on 3D skeletal data, combining Graph Spatio-Temporal Feature Representation (GSFR) with a Broad Learning System (BLS) to overcome these challenges. …”
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Introducing PtAC – an open source tool to assess SDG 11.2 using open data by Simon Nieland, Mirko Goletz, Daniel Krajzewicz, Daniela Palacios Lopez

“…Despite its global importance as the key indicator for urban mobility, many cities face significant barriers to implementation, including limited access to standardized input data and a lack of technical capacity. To address these challenges, we introduce PtAC, the Public Transport Access Calculator, an open-source Python library that combines remote sensing data from the World Settlement Footprint Population with crowdsourced geospatial data from OpenStreetMap. …”
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Experimental Demonstration of Continuous Sensor Data Monitoring Using Neural Network-Based Optical Camera Communications by Md. Faisal Ahmed, Moh. Khalid Hasan, Md. Shahjalal, Md. Morshed Alam, Yeong Min Jang

“…A modulation scheme standardized in IEEE 802.15.7, referred to as variable pulse-width modulation, is used to encode the data bits. The proposed scheme is flicker-free in different frequencies and applicable in both static and mobile scenarios. …”
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Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity by Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha, Mohamed Esha, Konguvel Elango, Vidhya Sampath

“…These findings validate the feasibility of privacy-preserving artificial intelligence models in real-world clinical applications, effectively balancing data protection with reliable medical predictions.…”
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